Existing energy systems are plagued by high variability in demand for power and the lack of effective control over the demand. For example, peak electrical consumption (in terms of wattage) is much higher than average consumption, but the total duration of peak consumption is relatively short. It can be costly to maintain the surge capacity that is only needed during peak consumption periods. As a result, utility companies often impose brown-outs and/or black-outs when capacity is insufficient. This practice has many negative impacts on the residents and businesses in the service area.
Some research has been done in recent years to develop more cost effective and less intrusive methods for easing the strains on existing energy systems. For example, studies have shown that there is a high level of flexibility in actual consumer requirements, and therefore it is possible in theory to reduce peak consumption without depriving consumers of energy they are unwilling to give up.
One conventional approach is to encourage consumers to conserve energy voluntarily by increasing their awareness of energy consumption. For example, studies have shown that information about energy consumption of consumers relative to their neighbors can cause high consumers to dramatically reduce their consumption. Also, studies conducted in California showed that consumers given a “mood ring” that indicates in real time the stress on the power grid dramatically reduced their peak-time consumption.
Another conventional approach is to use energy pricing, either in real or virtual currency, to gauge each consumer's willingness to reduce consumption. In these so-called market-based systems, the price of energy is allowed to fluctuate in real time based on actual demand, which provides an economic incentive for consumers to reduce consumption when the actual demand is high. The rationale behind these systems is that the price a consumer is willing to pay for energy is inversely related to the consumer's willingness to reduce energy consumption, so that a consumer who is more willing to reduce energy consumption will do so at a lower price point compared to another consumer who is less willing to reduce energy consumption. Thus, as the market finds equilibrium, the system approaches a desired state where each consumer reduces energy consumption only to the extent he is willing.
Conventional systems have also been developed to control energy demand related to heating and/or cooling in a building. Typically, these systems employ a centralized architecture where a central controller collects information from various sources and provides control signals to heating and/or cooling units based on the collected information.
New methods of demand-response management are desirable to overcome the shortcomings of conventional systems. New smart grid devices enable utilities to implement sophisticated demand-response programs. However, utilities are reluctant to undergo significant changes, such as implementing new methods of demand-response management, without significant corroboration of benefit and palpable sense of operation. Utilities would benefit from a system that allowed emulation of smart grid devices so that demand-response programs could be simulated and tested prior to implementation.
Current solutions for simulating demand-response programs in a smart grid comprise servers transmitting control broadcast messages which simply, and indiscriminately, tell virtual smart grid devices to turn themselves off. Current systems do not provide integrated emulation capabilities of virtual smart grid devices in a heterogeneous environment comprising both real and virtual smart grid devices. Current systems also lack control mechanisms and the ability to aggregate smart grid device status information.
Additionally, current systems do not provide privacy protection to end users, and only obtain gross results. Current systems use brute force mechanisms and are unable to determine how many smart grid devices responded to a demand-response control broadcast message.
It would be advantageous to provide a system for emulating the performance of smart grid devices in a smart grid in order to simulate a demand-response program in order to assess the efficacy of such program. It would also be advantageous to provide a system for emulating the performance of smart grid devices in a heterogeneous smart grid consisting of both real smart grid devices and virtual smart grid devices.
It would further be advantageous to provide a system for mass emulation of an arbitrary number of smart grid devices. It would further be advantageous to provide a method of optimizing the accuracy of the virtual mass emulator in a heterogeneous smart grid by including data from real smart grid devices in the virtual mass emulator's feedback loop.